Self-training for domain adaptation. Pseudo label-based self-training is a popular approach for unsupervised domain adaptation. In [1]}, RoyChowdhury propose a method that trains the object detection network FasterRCNN [2]} with a combination of high confidence pseudo labels and labels obtained from a tracker and a knowledge distillation loss to adapt the network for face detection. This approach relies on video data to obtain its tracking results. Khodabandeh [3]} propose Robust FasterRCNN, a domain adaptive 2D object detector that consists of a source-model training stage, a pseudo label generation stage using a pretrained image classifier for refinement, and a final stage where the detector network is trained on the source labels and the refined target pseudo-labels.
Yang put forth ST3D [4]}, a self training approach for 3D domain adaptive object detection where the network is adapted by training with a proposed curriculum data augmentation algorithm using pseudo-labels generated with a quality-aware memory bank. While showing promising results in some cases, in others this method is outperformed by standard pseudo-label based self-training, and depends on boosting with statistical normalization [5]}, a weakly supervised method, for its best reported results. Caine [6]} propose a simple, yet effective domain adaptation method for 3D object detection using the base network of Pointpillars [7]} that trains a student network with a combination of source labels and target pseudo labels obtained from a teacher network trained on the labeled source data. However, simple thresholding methods for pseudo-label collection from the source model may lead to training the model with incorrect labels that have high confidence and discarding correct labels with confidence that falls below the threshold.
